Overview

Dataset statistics

Number of variables24
Number of observations284928
Missing cells2688
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory130.2 MiB
Average record size in memory479.3 B

Variable types

NUM19
CAT5

Warnings

id has a high cardinality: 106 distinct values High cardinality
roi has a high cardinality: 235 distinct values High cardinality
age_yrs is highly correlated with age_mosHigh correlation
age_mos is highly correlated with age_yrsHigh correlation
id is uniformly distributed Uniform
band is uniformly distributed Uniform

Reproduction

Analysis started2020-10-13 21:28:22.315742
Analysis finished2020-10-13 21:29:34.960494
Duration1 minute and 12.64 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

id
Categorical

HIGH CARDINALITY
UNIFORM

Distinct106
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
507_17a
 
2688
309_13a
 
2688
411_15a
 
2688
417_15a
 
2688
133_9a
 
2688
Other values (101)
271488 
ValueCountFrequency (%) 
507_17a26880.9%
 
309_13a26880.9%
 
411_15a26880.9%
 
417_15a26880.9%
 
133_9a26880.9%
 
526_17a26880.9%
 
216_11a26880.9%
 
227_11a26880.9%
 
525_17a26880.9%
 
522_17a26880.9%
 
221_11a26880.9%
 
314_13a26880.9%
 
410_15a26880.9%
 
225_11a26880.9%
 
512_17a26880.9%
 
323_13a26880.9%
 
401_15a26880.9%
 
308_13a26880.9%
 
224_11a26880.9%
 
123_9a26880.9%
 
333_13a26880.9%
 
514_17a26880.9%
 
406_15a26880.9%
 
218_11a26880.9%
 
210_11a26880.9%
 
Other values (81)21772876.4%
 
2020-10-13T14:29:35.062671image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-13T14:29:35.159654image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length7
Median length7
Mean length6.858490566
Min length6

Overview of Unicode Properties

Unique unicode characters12
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
146771223.9%
 
_28492814.6%
 
a28492814.6%
 
220966410.7%
 
319891210.2%
 
51290246.6%
 
01048325.4%
 
7833284.3%
 
4698883.6%
 
9645123.3%
 
6322561.7%
 
8241921.2%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number138432070.8%
 
Connector Punctuation28492814.6%
 
Lowercase Letter28492814.6%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
146771233.8%
 
220966415.1%
 
319891214.4%
 
51290249.3%
 
01048327.6%
 
7833286.0%
 
4698885.0%
 
9645124.7%
 
6322562.3%
 
8241921.7%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_284928100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a284928100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common166924885.4%
 
Latin28492814.6%
 

Most frequent Common characters

ValueCountFrequency (%) 
146771228.0%
 
_28492817.1%
 
220966412.6%
 
319891211.9%
 
51290247.7%
 
01048326.3%
 
7833285.0%
 
4698884.2%
 
9645123.9%
 
6322561.9%
 
8241921.4%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a284928100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1954176100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
146771223.9%
 
_28492814.6%
 
a28492814.6%
 
220966410.7%
 
319891210.2%
 
51290246.6%
 
01048325.4%
 
7833284.3%
 
4698883.6%
 
9645123.3%
 
6322561.7%
 
8241921.2%
 

age_mos
Real number (ℝ≥0)

HIGH CORRELATION

Distinct72
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean159.5240566
Minimum109.5
Maximum207.7
Zeros0
Zeros (%)0.0%
Memory size4.3 MiB
2020-10-13T14:29:35.262650image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum109.5
5-th percentile110.3
Q1134.35
median158.3
Q3183.2
95-th percentile207
Maximum207.7
Range98.2
Interquartile range (IQR)48.85

Descriptive statistics

Standard deviation32.80884033
Coefficient of variation (CV)0.2056670387
Kurtosis-1.210376782
Mean159.5240566
Median Absolute Deviation (MAD)24.65
Skewness0.114834434
Sum45452870.4
Variance1076.420004
MonotocityNot monotonic
2020-10-13T14:29:35.377385image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
134.7134404.7%
 
158.9134404.7%
 
158.3107523.8%
 
110.580642.8%
 
158.580642.8%
 
134.880642.8%
 
134.6153761.9%
 
207.753761.9%
 
183.153761.9%
 
20653761.9%
 
15853761.9%
 
133.453761.9%
 
206.753761.9%
 
206.253761.9%
 
110.453761.9%
 
110.653761.9%
 
158.853761.9%
 
205.353761.9%
 
206.653761.9%
 
206.153761.9%
 
18353761.9%
 
181.553761.9%
 
157.453761.9%
 
15926880.9%
 
134.3526880.9%
 
Other values (47)12633644.3%
 
ValueCountFrequency (%) 
109.526880.9%
 
109.726880.9%
 
109.926880.9%
 
110.126880.9%
 
110.226880.9%
 
110.326880.9%
 
110.453761.9%
 
110.580642.8%
 
110.653761.9%
 
110.726880.9%
 
ValueCountFrequency (%) 
207.753761.9%
 
207.426880.9%
 
207.126880.9%
 
207.0726880.9%
 
20726880.9%
 
206.826880.9%
 
206.7126880.9%
 
206.753761.9%
 
206.653761.9%
 
206.526880.9%
 

age_yrs
Real number (ℝ≥0)

HIGH CORRELATION

Distinct56
Distinct (%)< 0.1%
Missing2688
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean13.2332381
Minimum9.07
Maximum17.2
Zeros0
Zeros (%)0.0%
Memory size4.3 MiB
2020-10-13T14:29:35.487370image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum9.07
5-th percentile9.13
Q111.13
median13.11
Q315.19
95-th percentile17.14
Maximum17.2
Range8.13
Interquartile range (IQR)4.06

Descriptive statistics

Standard deviation2.722901214
Coefficient of variation (CV)0.2057622779
Kurtosis-1.214373646
Mean13.2332381
Median Absolute Deviation (MAD)2.05
Skewness0.09677161999
Sum3734949.12
Variance7.414191022
MonotocityNot monotonic
2020-10-13T14:29:35.604603image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
11.16215047.5%
 
13.16161285.7%
 
13.11134404.7%
 
17.0780642.8%
 
17.0880642.8%
 
9.1680642.8%
 
13.1380642.8%
 
17.1180642.8%
 
17.1280642.8%
 
11.1580642.8%
 
13.0380642.8%
 
11.1380642.8%
 
11.0653761.9%
 
9.1353761.9%
 
17.253761.9%
 
15.253761.9%
 
13.0853761.9%
 
15.1653761.9%
 
11.153761.9%
 
9.1553761.9%
 
9.1453761.9%
 
17.1553761.9%
 
13.0953761.9%
 
15.0353761.9%
 
11.0153761.9%
 
Other values (31)8870431.1%
 
ValueCountFrequency (%) 
9.0726880.9%
 
9.0826880.9%
 
9.126880.9%
 
9.1226880.9%
 
9.1353761.9%
 
9.1453761.9%
 
9.1553761.9%
 
9.1680642.8%
 
9.1726880.9%
 
9.1826880.9%
 
ValueCountFrequency (%) 
17.253761.9%
 
17.1826880.9%
 
17.1553761.9%
 
17.1426880.9%
 
17.1326880.9%
 
17.1280642.8%
 
17.1180642.8%
 
17.0880642.8%
 
17.0780642.8%
 
17.0626880.9%
 

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
female
147840 
male
137088 
ValueCountFrequency (%) 
female14784051.9%
 
male13708848.1%
 
2020-10-13T14:29:35.704999image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-13T14:29:35.755067image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:29:35.829613image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length6
Mean length5.037735849
Min length4

Overview of Unicode Properties

Unique unicode characters5
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
e43276830.1%
 
m28492819.9%
 
a28492819.9%
 
l28492819.9%
 
f14784010.3%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter1435392100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e43276830.1%
 
m28492819.9%
 
a28492819.9%
 
l28492819.9%
 
f14784010.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin1435392100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e43276830.1%
 
m28492819.9%
 
a28492819.9%
 
l28492819.9%
 
f14784010.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1435392100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
e43276830.1%
 
m28492819.9%
 
a28492819.9%
 
l28492819.9%
 
f14784010.3%
 

peermindset
Real number (ℝ≥0)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.191823899
Minimum1
Maximum4.583333333
Zeros0
Zeros (%)0.0%
Memory size4.3 MiB
2020-10-13T14:29:35.921534image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.166666667
Q11.75
median2.166666667
Q32.583333333
95-th percentile3.25
Maximum4.583333333
Range3.583333333
Interquartile range (IQR)0.8333333333

Descriptive statistics

Standard deviation0.6547861702
Coefficient of variation (CV)0.2987403187
Kurtosis0.8767724878
Mean2.191823899
Median Absolute Deviation (MAD)0.4166666667
Skewness0.5545970933
Sum624512
Variance0.4287449287
MonotocityNot monotonic
2020-10-13T14:29:36.021125image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%) 
2.416666667241928.5%
 
2.166666667215047.5%
 
2.25188166.6%
 
2.666666667188166.6%
 
1.583333333188166.6%
 
2.083333333134404.7%
 
2134404.7%
 
1.916666667134404.7%
 
2.333333333134404.7%
 
2.75134404.7%
 
1.416666667107523.8%
 
1.833333333107523.8%
 
1107523.8%
 
3.2580642.8%
 
1.2580642.8%
 
1.553761.9%
 
1.33333333353761.9%
 
2.83333333353761.9%
 
2.58333333353761.9%
 
1.66666666753761.9%
 
3.41666666753761.9%
 
2.553761.9%
 
353761.9%
 
1.7553761.9%
 
3.66666666726880.9%
 
Other values (6)161285.7%
 
ValueCountFrequency (%) 
1107523.8%
 
1.08333333326880.9%
 
1.16666666726880.9%
 
1.2580642.8%
 
1.33333333353761.9%
 
1.416666667107523.8%
 
1.553761.9%
 
1.583333333188166.6%
 
1.66666666753761.9%
 
1.7553761.9%
 
ValueCountFrequency (%) 
4.58333333326880.9%
 
3.91666666726880.9%
 
3.66666666726880.9%
 
3.41666666753761.9%
 
3.2580642.8%
 
3.16666666726880.9%
 
3.08333333326880.9%
 
353761.9%
 
2.83333333353761.9%
 
2.75134404.7%
 

persmindset
Real number (ℝ≥0)

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.885220126
Minimum1
Maximum4.833333333
Zeros0
Zeros (%)0.0%
Memory size4.3 MiB
2020-10-13T14:29:36.116674image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11.333333333
median1.833333333
Q32.333333333
95-th percentile3.166666667
Maximum4.833333333
Range3.833333333
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.6857971983
Coefficient of variation (CV)0.3637756615
Kurtosis2.591130297
Mean1.885220126
Median Absolute Deviation (MAD)0.5
Skewness1.232196282
Sum537152
Variance0.4703177972
MonotocityNot monotonic
2020-10-13T14:29:36.207581image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%) 
24300815.1%
 
1.8333333333763213.2%
 
13494412.3%
 
1.52956810.4%
 
2.333333333268809.4%
 
1.666666667241928.5%
 
1.333333333215047.5%
 
1.166666667161285.7%
 
2.666666667134404.7%
 
2.83333333380642.8%
 
2.580642.8%
 
3.33333333353761.9%
 
426880.9%
 
2.16666666726880.9%
 
3.16666666726880.9%
 
3.526880.9%
 
4.83333333326880.9%
 
326880.9%
 
ValueCountFrequency (%) 
13494412.3%
 
1.166666667161285.7%
 
1.333333333215047.5%
 
1.52956810.4%
 
1.666666667241928.5%
 
1.8333333333763213.2%
 
24300815.1%
 
2.16666666726880.9%
 
2.333333333268809.4%
 
2.580642.8%
 
ValueCountFrequency (%) 
4.83333333326880.9%
 
426880.9%
 
3.526880.9%
 
3.33333333353761.9%
 
3.16666666726880.9%
 
326880.9%
 
2.83333333380642.8%
 
2.666666667134404.7%
 
2.580642.8%
 
2.333333333268809.4%
 

needforapproval
Real number (ℝ≥0)

Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.650943396
Minimum1.875
Maximum5.375
Zeros0
Zeros (%)0.0%
Memory size4.3 MiB
2020-10-13T14:29:36.299641image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1.875
5-th percentile2.25
Q13.25
median3.625
Q34.125
95-th percentile4.75
Maximum5.375
Range3.5
Interquartile range (IQR)0.875

Descriptive statistics

Standard deviation0.7412467829
Coefficient of variation (CV)0.2030288346
Kurtosis-0.2104096771
Mean3.650943396
Median Absolute Deviation (MAD)0.5
Skewness-0.1374357943
Sum1040256
Variance0.5494467931
MonotocityNot monotonic
2020-10-13T14:29:36.395945image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%) 
3.25268809.4%
 
4.125241928.5%
 
3.875215047.5%
 
4.375215047.5%
 
3.375188166.6%
 
3.5188166.6%
 
3.125161285.7%
 
3.75161285.7%
 
2.875134404.7%
 
4.25134404.7%
 
3.625107523.8%
 
4.75107523.8%
 
380642.8%
 
2.2580642.8%
 
480642.8%
 
2.37553761.9%
 
4.62553761.9%
 
2.7553761.9%
 
4.87553761.9%
 
4.553761.9%
 
2.62553761.9%
 
5.2553761.9%
 
1.87553761.9%
 
5.37526880.9%
 
226880.9%
 
ValueCountFrequency (%) 
1.87553761.9%
 
226880.9%
 
2.2580642.8%
 
2.37553761.9%
 
2.62553761.9%
 
2.7553761.9%
 
2.875134404.7%
 
380642.8%
 
3.125161285.7%
 
3.25268809.4%
 
ValueCountFrequency (%) 
5.37526880.9%
 
5.2553761.9%
 
4.87553761.9%
 
4.75107523.8%
 
4.62553761.9%
 
4.553761.9%
 
4.375215047.5%
 
4.25134404.7%
 
4.125241928.5%
 
480642.8%
 

needforbelonging
Real number (ℝ≥0)

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.874528302
Minimum1.8
Maximum5.3
Zeros0
Zeros (%)0.0%
Memory size4.3 MiB
2020-10-13T14:29:36.487492image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1.8
5-th percentile2.8
Q13.4
median4
Q34.4
95-th percentile4.8
Maximum5.3
Range3.5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.6473558871
Coefficient of variation (CV)0.1670799222
Kurtosis0.01674383508
Mean3.874528302
Median Absolute Deviation (MAD)0.4
Skewness-0.4236589392
Sum1103961.6
Variance0.4190696445
MonotocityNot monotonic
2020-10-13T14:29:36.581036image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%) 
4.4241928.5%
 
4.1241928.5%
 
3.8188166.6%
 
4.2188166.6%
 
4161285.7%
 
3.3161285.7%
 
3.6134404.7%
 
4.5134404.7%
 
3.9134404.7%
 
4.6107523.8%
 
3107523.8%
 
4.7107523.8%
 
4.3107523.8%
 
3.1107523.8%
 
3.7107523.8%
 
3.480642.8%
 
3.580642.8%
 
2.880642.8%
 
553761.9%
 
3.253761.9%
 
2.953761.9%
 
4.853761.9%
 
5.226880.9%
 
5.326880.9%
 
2.726880.9%
 
Other values (3)80642.8%
 
ValueCountFrequency (%) 
1.826880.9%
 
2.426880.9%
 
2.526880.9%
 
2.726880.9%
 
2.880642.8%
 
2.953761.9%
 
3107523.8%
 
3.1107523.8%
 
3.253761.9%
 
3.3161285.7%
 
ValueCountFrequency (%) 
5.326880.9%
 
5.226880.9%
 
553761.9%
 
4.853761.9%
 
4.7107523.8%
 
4.6107523.8%
 
4.5134404.7%
 
4.4241928.5%
 
4.3107523.8%
 
4.2188166.6%
 

rejection
Real number (ℝ≥0)

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.273584906
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Memory size4.3 MiB
2020-10-13T14:29:36.663947image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12.5
median3.5
Q34
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation1.073112499
Coefficient of variation (CV)0.3278095818
Kurtosis-0.3171434419
Mean3.273584906
Median Absolute Deviation (MAD)0.5
Skewness-0.5199409074
Sum932736
Variance1.151570435
MonotocityNot monotonic
2020-10-13T14:29:36.749599image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%) 
3.56988824.5%
 
45107217.9%
 
33494412.3%
 
2.52956810.4%
 
4.5268809.4%
 
1241928.5%
 
2241928.5%
 
5215047.5%
 
1.526880.9%
 
ValueCountFrequency (%) 
1241928.5%
 
1.526880.9%
 
2241928.5%
 
2.52956810.4%
 
33494412.3%
 
3.56988824.5%
 
45107217.9%
 
4.5268809.4%
 
5215047.5%
 
ValueCountFrequency (%) 
5215047.5%
 
4.5268809.4%
 
45107217.9%
 
3.56988824.5%
 
33494412.3%
 
2.52956810.4%
 
2241928.5%
 
1.526880.9%
 
1241928.5%
 

coping_mad
Real number (ℝ≥0)

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.025157233
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Memory size4.3 MiB
2020-10-13T14:29:36.832898image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.666666667
Q12.333333333
median3
Q33.666666667
95-th percentile4.333333333
Maximum5
Range4
Interquartile range (IQR)1.333333333

Descriptive statistics

Standard deviation0.8476126493
Coefficient of variation (CV)0.2801879652
Kurtosis-0.6106739343
Mean3.025157233
Median Absolute Deviation (MAD)0.6666666667
Skewness-0.2325111782
Sum861952
Variance0.7184472033
MonotocityNot monotonic
2020-10-13T14:29:36.915664image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%) 
2.6666666674569616.0%
 
3.6666666674032014.2%
 
44032014.2%
 
3.3333333333763213.2%
 
32956810.4%
 
2.333333333268809.4%
 
2188166.6%
 
1.666666667161285.7%
 
1.333333333107523.8%
 
4.333333333107523.8%
 
126880.9%
 
4.66666666726880.9%
 
526880.9%
 
ValueCountFrequency (%) 
126880.9%
 
1.333333333107523.8%
 
1.666666667161285.7%
 
2188166.6%
 
2.333333333268809.4%
 
2.6666666674569616.0%
 
32956810.4%
 
3.3333333333763213.2%
 
3.6666666674032014.2%
 
44032014.2%
 
ValueCountFrequency (%) 
526880.9%
 
4.66666666726880.9%
 
4.333333333107523.8%
 
44032014.2%
 
3.6666666674032014.2%
 
3.3333333333763213.2%
 
32956810.4%
 
2.6666666674569616.0%
 
2.333333333268809.4%
 
2188166.6%
 

coping_sad
Real number (ℝ≥0)

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.232704403
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Memory size4.3 MiB
2020-10-13T14:29:36.998053image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.333333333
Q12.333333333
median3.333333333
Q34
95-th percentile4.666666667
Maximum5
Range4
Interquartile range (IQR)1.666666667

Descriptive statistics

Standard deviation0.9991311556
Coefficient of variation (CV)0.3090697544
Kurtosis-0.7063764819
Mean3.232704403
Median Absolute Deviation (MAD)0.6666666667
Skewness-0.3701458331
Sum921088
Variance0.9982630661
MonotocityNot monotonic
2020-10-13T14:29:37.083723image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%) 
44838417.0%
 
3.6666666674032014.2%
 
3.3333333333225611.3%
 
2.666666667268809.4%
 
2268809.4%
 
4.333333333188166.6%
 
2.333333333188166.6%
 
3188166.6%
 
4.666666667188166.6%
 
1.666666667107523.8%
 
1.33333333380642.8%
 
180642.8%
 
580642.8%
 
ValueCountFrequency (%) 
180642.8%
 
1.33333333380642.8%
 
1.666666667107523.8%
 
2268809.4%
 
2.333333333188166.6%
 
2.666666667268809.4%
 
3188166.6%
 
3.3333333333225611.3%
 
3.6666666674032014.2%
 
44838417.0%
 
ValueCountFrequency (%) 
580642.8%
 
4.666666667188166.6%
 
4.333333333188166.6%
 
44838417.0%
 
3.6666666674032014.2%
 
3.3333333333225611.3%
 
3188166.6%
 
2.666666667268809.4%
 
2.333333333188166.6%
 
2268809.4%
 

coping_worried
Real number (ℝ≥0)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.600628931
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Memory size4.3 MiB
2020-10-13T14:29:37.173085image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2.666666667
Q33.333333333
95-th percentile4
Maximum5
Range4
Interquartile range (IQR)1.333333333

Descriptive statistics

Standard deviation0.8824174852
Coefficient of variation (CV)0.3393092628
Kurtosis-0.5405999593
Mean2.600628931
Median Absolute Deviation (MAD)0.6666666667
Skewness0.02508212248
Sum740992
Variance0.7786606182
MonotocityNot monotonic
2020-10-13T14:29:37.256189image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%) 
34569616.0%
 
3.3333333334032014.2%
 
2.3333333333225611.3%
 
2.6666666673225611.3%
 
1.6666666672956810.4%
 
22956810.4%
 
3.666666667215047.5%
 
1188166.6%
 
1.333333333161285.7%
 
4.33333333380642.8%
 
480642.8%
 
526880.9%
 
ValueCountFrequency (%) 
1188166.6%
 
1.333333333161285.7%
 
1.6666666672956810.4%
 
22956810.4%
 
2.3333333333225611.3%
 
2.6666666673225611.3%
 
34569616.0%
 
3.3333333334032014.2%
 
3.666666667215047.5%
 
480642.8%
 
ValueCountFrequency (%) 
526880.9%
 
4.33333333380642.8%
 
480642.8%
 
3.666666667215047.5%
 
3.3333333334032014.2%
 
34569616.0%
 
2.6666666673225611.3%
 
2.3333333333225611.3%
 
22956810.4%
 
1.6666666672956810.4%
 

rsqanxiety
Real number (ℝ≥0)

Distinct80
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.829402516
Minimum1.916666667
Maximum22.5
Zeros0
Zeros (%)0.0%
Memory size4.3 MiB
2020-10-13T14:29:37.356422image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1.916666667
5-th percentile3.166666667
Q16
median8.916666667
Q311.16666667
95-th percentile15.25
Maximum22.5
Range20.58333333
Interquartile range (IQR)5.166666667

Descriptive statistics

Standard deviation3.818345951
Coefficient of variation (CV)0.4324580224
Kurtosis0.5385469472
Mean8.829402516
Median Absolute Deviation (MAD)2.5
Skewness0.5381413152
Sum2515744
Variance14.5797658
MonotocityNot monotonic
2020-10-13T14:29:37.462557image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
9.833333333134404.7%
 
8.916666667107523.8%
 
7.41666666780642.8%
 
12.2580642.8%
 
9.16666666780642.8%
 
5.08333333353761.9%
 
11.4166666753761.9%
 
6.91666666753761.9%
 
11.1666666753761.9%
 
10.6666666753761.9%
 
6.7553761.9%
 
653761.9%
 
10.4166666753761.9%
 
7.16666666753761.9%
 
3.66666666753761.9%
 
4.33333333353761.9%
 
9.33333333353761.9%
 
3.91666666753761.9%
 
926880.9%
 
4.7526880.9%
 
11.2526880.9%
 
6.41666666726880.9%
 
1126880.9%
 
13.0833333326880.9%
 
3.33333333326880.9%
 
Other values (55)14784051.9%
 
ValueCountFrequency (%) 
1.91666666726880.9%
 
226880.9%
 
2.16666666726880.9%
 
2.526880.9%
 
2.83333333326880.9%
 
3.16666666726880.9%
 
3.33333333326880.9%
 
3.66666666753761.9%
 
3.91666666753761.9%
 
4.16666666726880.9%
 
ValueCountFrequency (%) 
22.526880.9%
 
17.7526880.9%
 
17.0833333326880.9%
 
16.526880.9%
 
16.4166666726880.9%
 
15.2526880.9%
 
14.7526880.9%
 
14.526880.9%
 
14.3333333326880.9%
 
14.1666666726880.9%
 

rsqanger
Real number (ℝ≥0)

Distinct66
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.948113208
Minimum1.916666667
Maximum15.33333333
Zeros0
Zeros (%)0.0%
Memory size4.3 MiB
2020-10-13T14:29:37.561371image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1.916666667
5-th percentile2.5
Q13.666666667
median5.25
Q37.75
95-th percentile11.16666667
Maximum15.33333333
Range13.41666667
Interquartile range (IQR)4.083333333

Descriptive statistics

Standard deviation2.865125754
Coefficient of variation (CV)0.4816864868
Kurtosis0.4050794883
Mean5.948113208
Median Absolute Deviation (MAD)1.791666667
Skewness0.920731791
Sum1694784
Variance8.208945586
MonotocityNot monotonic
2020-10-13T14:29:37.679883image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
3.25107523.8%
 
3.666666667107523.8%
 
4.25107523.8%
 
9.833333333107523.8%
 
6.41666666780642.8%
 
3.580642.8%
 
4.91666666780642.8%
 
3.7580642.8%
 
4.41666666780642.8%
 
7.33333333380642.8%
 
2.66666666780642.8%
 
853761.9%
 
5.41666666753761.9%
 
5.2553761.9%
 
3.83333333353761.9%
 
6.58333333353761.9%
 
2.7553761.9%
 
453761.9%
 
4.553761.9%
 
5.7553761.9%
 
2.41666666753761.9%
 
3.16666666753761.9%
 
5.33333333353761.9%
 
9.91666666753761.9%
 
8.41666666753761.9%
 
Other values (41)11020838.7%
 
ValueCountFrequency (%) 
1.91666666726880.9%
 
226880.9%
 
2.2526880.9%
 
2.41666666753761.9%
 
2.526880.9%
 
2.58333333326880.9%
 
2.66666666780642.8%
 
2.7553761.9%
 
3.08333333326880.9%
 
3.16666666753761.9%
 
ValueCountFrequency (%) 
15.3333333326880.9%
 
14.0833333326880.9%
 
13.0833333326880.9%
 
12.6666666726880.9%
 
11.526880.9%
 
11.1666666726880.9%
 
10.8333333326880.9%
 
1026880.9%
 
9.91666666753761.9%
 
9.833333333107523.8%
 

cdimean
Real number (ℝ≥0)

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.233962264
Minimum1
Maximum2.7
Zeros0
Zeros (%)0.0%
Memory size4.3 MiB
2020-10-13T14:29:37.768285image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11.1
median1.2
Q31.4
95-th percentile1.7
Maximum2.7
Range1.7
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.2623867494
Coefficient of variation (CV)0.2126375798
Kurtosis8.291559911
Mean1.233962264
Median Absolute Deviation (MAD)0.15
Skewness2.279229604
Sum351590.4
Variance0.06884680624
MonotocityNot monotonic
2020-10-13T14:29:38.231053image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%) 
16988824.5%
 
1.16720023.6%
 
1.25376018.9%
 
1.4241928.5%
 
1.3215047.5%
 
1.5188166.6%
 
1.7107523.8%
 
1.6107523.8%
 
2.726880.9%
 
1.826880.9%
 
2.126880.9%
 
ValueCountFrequency (%) 
16988824.5%
 
1.16720023.6%
 
1.25376018.9%
 
1.3215047.5%
 
1.4241928.5%
 
1.5188166.6%
 
1.6107523.8%
 
1.7107523.8%
 
1.826880.9%
 
2.126880.9%
 
ValueCountFrequency (%) 
2.726880.9%
 
2.126880.9%
 
1.826880.9%
 
1.7107523.8%
 
1.6107523.8%
 
1.5188166.6%
 
1.4241928.5%
 
1.3215047.5%
 
1.25376018.9%
 
1.16720023.6%
 

moodgood
Real number (ℝ≥0)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.358490566
Minimum2
Maximum7
Zeros0
Zeros (%)0.0%
Memory size4.3 MiB
2020-10-13T14:29:38.310003image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q14
median6
Q36
95-th percentile7
Maximum7
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.091898995
Coefficient of variation (CV)0.2037698829
Kurtosis-0.4780605424
Mean5.358490566
Median Absolute Deviation (MAD)1
Skewness-0.4406022905
Sum1526784
Variance1.192243415
MonotocityNot monotonic
2020-10-13T14:29:38.387962image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%) 
612096042.5%
 
46988824.5%
 
55107217.9%
 
73494412.3%
 
353761.9%
 
226880.9%
 
ValueCountFrequency (%) 
226880.9%
 
353761.9%
 
46988824.5%
 
55107217.9%
 
612096042.5%
 
73494412.3%
 
ValueCountFrequency (%) 
73494412.3%
 
612096042.5%
 
55107217.9%
 
46988824.5%
 
353761.9%
 
226880.9%
 

moodhappy
Real number (ℝ≥0)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.113207547
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Memory size4.3 MiB
2020-10-13T14:29:38.456772image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q14
median5
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.21562958
Coefficient of variation (CV)0.2377430544
Kurtosis-0.1846606546
Mean5.113207547
Median Absolute Deviation (MAD)1
Skewness-0.4377388523
Sum1456896
Variance1.477755275
MonotocityNot monotonic
2020-10-13T14:29:38.530636image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%) 
610214435.8%
 
47526426.4%
 
55376018.9%
 
72956810.4%
 
3215047.5%
 
126880.9%
 
ValueCountFrequency (%) 
126880.9%
 
3215047.5%
 
47526426.4%
 
55376018.9%
 
610214435.8%
 
72956810.4%
 
ValueCountFrequency (%) 
72956810.4%
 
610214435.8%
 
55376018.9%
 
47526426.4%
 
3215047.5%
 
126880.9%
 

moodrelaxed
Real number (ℝ≥0)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.622641509
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Memory size4.3 MiB
2020-10-13T14:29:38.600687image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median5
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.450134811
Coefficient of variation (CV)0.3137026325
Kurtosis-1.06783578
Mean4.622641509
Median Absolute Deviation (MAD)1
Skewness-0.2535507409
Sum1317120
Variance2.102890969
MonotocityNot monotonic
2020-10-13T14:29:38.671488image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
69139232.1%
 
36988824.5%
 
55107217.9%
 
44032014.2%
 
7161285.7%
 
2134404.7%
 
126880.9%
 
ValueCountFrequency (%) 
126880.9%
 
2134404.7%
 
36988824.5%
 
44032014.2%
 
55107217.9%
 
69139232.1%
 
7161285.7%
 
ValueCountFrequency (%) 
7161285.7%
 
69139232.1%
 
55107217.9%
 
44032014.2%
 
36988824.5%
 
2134404.7%
 
126880.9%
 

stateanxiety
Real number (ℝ≥0)

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.03773585
Minimum20
Maximum50
Zeros0
Zeros (%)0.0%
Memory size4.3 MiB
2020-10-13T14:29:38.748698image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile26
Q128
median31
Q333
95-th percentile38
Maximum50
Range30
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.452957925
Coefficient of variation (CV)0.1434691611
Kurtosis2.914742921
Mean31.03773585
Median Absolute Deviation (MAD)2
Skewness0.7346693196
Sum8843520
Variance19.82883428
MonotocityNot monotonic
2020-10-13T14:29:38.835469image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%) 
314300815.1%
 
283225611.3%
 
292956810.4%
 
302956810.4%
 
32268809.4%
 
34215047.5%
 
33188166.6%
 
26161285.7%
 
38134404.7%
 
27107523.8%
 
35107523.8%
 
3780642.8%
 
2153761.9%
 
2053761.9%
 
5026880.9%
 
2426880.9%
 
4026880.9%
 
4326880.9%
 
4226880.9%
 
ValueCountFrequency (%) 
2053761.9%
 
2153761.9%
 
2426880.9%
 
26161285.7%
 
27107523.8%
 
283225611.3%
 
292956810.4%
 
302956810.4%
 
314300815.1%
 
32268809.4%
 
ValueCountFrequency (%) 
5026880.9%
 
4326880.9%
 
4226880.9%
 
4026880.9%
 
38134404.7%
 
3780642.8%
 
35107523.8%
 
34215047.5%
 
33188166.6%
 
32268809.4%
 

traitanxiety
Real number (ℝ≥0)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.95283019
Minimum22
Maximum60
Zeros0
Zeros (%)0.0%
Memory size4.3 MiB
2020-10-13T14:29:38.926007image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile23
Q130
median34.5
Q342
95-th percentile49
Maximum60
Range38
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.145370739
Coefficient of variation (CV)0.2265571499
Kurtosis-0.3953522955
Mean35.95283019
Median Absolute Deviation (MAD)6
Skewness0.369635291
Sum10243968
Variance66.34706447
MonotocityNot monotonic
2020-10-13T14:29:39.021750image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%) 
32241928.5%
 
33188166.6%
 
38161285.7%
 
30161285.7%
 
34161285.7%
 
43134404.7%
 
41134404.7%
 
31107523.8%
 
49107523.8%
 
26107523.8%
 
22107523.8%
 
37107523.8%
 
36107523.8%
 
35107523.8%
 
45107523.8%
 
2580642.8%
 
2880642.8%
 
4053761.9%
 
4253761.9%
 
2453761.9%
 
2353761.9%
 
4753761.9%
 
4653761.9%
 
4853761.9%
 
5153761.9%
 
Other values (6)215047.5%
 
ValueCountFrequency (%) 
22107523.8%
 
2353761.9%
 
2453761.9%
 
2580642.8%
 
26107523.8%
 
2753761.9%
 
2880642.8%
 
2926880.9%
 
30161285.7%
 
31107523.8%
 
ValueCountFrequency (%) 
6026880.9%
 
5426880.9%
 
5153761.9%
 
5026880.9%
 
49107523.8%
 
4853761.9%
 
4753761.9%
 
4653761.9%
 
45107523.8%
 
4453761.9%
 

band
Categorical

UNIFORM

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
beta
47488 
delta
47488 
gamma
47488 
DC
47488 
alpha
47488 
ValueCountFrequency (%) 
beta4748816.7%
 
delta4748816.7%
 
gamma4748816.7%
 
DC4748816.7%
 
alpha4748816.7%
 
theta4748816.7%
 
2020-10-13T14:29:39.116399image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-13T14:29:39.172806image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:29:39.256722image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length5
Median length5
Mean length4.333333333
Min length2

Overview of Unicode Properties

Unique unicode characters12
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
a33241626.9%
 
t18995215.4%
 
e14246411.5%
 
l949767.7%
 
h949767.7%
 
m949767.7%
 
D474883.8%
 
C474883.8%
 
d474883.8%
 
p474883.8%
 
b474883.8%
 
g474883.8%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter113971292.3%
 
Uppercase Letter949767.7%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
D4748850.0%
 
C4748850.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a33241629.2%
 
t18995216.7%
 
e14246412.5%
 
l949768.3%
 
h949768.3%
 
m949768.3%
 
d474884.2%
 
p474884.2%
 
b474884.2%
 
g474884.2%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin1234688100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a33241626.9%
 
t18995215.4%
 
e14246411.5%
 
l949767.7%
 
h949767.7%
 
m949767.7%
 
D474883.8%
 
C474883.8%
 
d474883.8%
 
p474883.8%
 
b474883.8%
 
g474883.8%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1234688100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
a33241626.9%
 
t18995215.4%
 
e14246411.5%
 
l949767.7%
 
h949767.7%
 
m949767.7%
 
D474883.8%
 
C474883.8%
 
d474883.8%
 
p474883.8%
 
b474883.8%
 
g474883.8%
 

roi
Categorical

HIGH CARDINALITY

Distinct235
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
superiorfrontal_4
 
1272
superiorfrontal_14
 
1272
superiorfrontal_15
 
1272
superiortemporal_5
 
1272
precentral_6
 
1272
Other values (230)
278568 
ValueCountFrequency (%) 
superiorfrontal_412720.4%
 
superiorfrontal_1412720.4%
 
superiorfrontal_1512720.4%
 
superiortemporal_512720.4%
 
precentral_612720.4%
 
superiorparietal_812720.4%
 
posteriorcingulate_212720.4%
 
lingual_412720.4%
 
precuneus_112720.4%
 
parstriangularis_112720.4%
 
cuneus_312720.4%
 
lateraloccipital_712720.4%
 
precentral_312720.4%
 
fusiform_312720.4%
 
precentral_1412720.4%
 
rostralmiddlefrontal_212720.4%
 
precentral_712720.4%
 
precuneus_212720.4%
 
fusiform_712720.4%
 
bankssts_212720.4%
 
fusiform_212720.4%
 
fusiform_812720.4%
 
supramarginal_312720.4%
 
parsopercularis_212720.4%
 
posteriorcingulate_312720.4%
 
Other values (210)25312888.8%
 
2020-10-13T14:29:39.384768image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-13T14:29:39.505867image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length26
Median length17
Mean length16.0625
Min length8

Overview of Unicode Properties

Unique unicode characters31
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
r55014012.0%
 
a45601210.0%
 
l3777848.3%
 
e3485287.6%
 
i3148206.9%
 
t3014646.6%
 
o2957406.5%
 
_2849286.2%
 
p2486765.4%
 
n2143324.7%
 
s1882564.1%
 
u1666323.6%
 
c1303802.8%
 
m1055762.3%
 
f1055762.3%
 
1973082.1%
 
d826801.8%
 
2457921.0%
 
g400680.9%
 
3394320.9%
 
4324360.7%
 
5267120.6%
 
6241680.5%
 
b216240.5%
 
7216240.5%
 
Other values (6)559681.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter396228086.6%
 
Decimal Number3294487.2%
 
Connector Punctuation2849286.2%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
r55014013.9%
 
a45601211.5%
 
l3777849.5%
 
e3485288.8%
 
i3148207.9%
 
t3014647.6%
 
o2957407.5%
 
p2486766.3%
 
n2143325.4%
 
s1882564.8%
 
u1666324.2%
 
c1303803.3%
 
m1055762.7%
 
f1055762.7%
 
d826802.1%
 
g400681.0%
 
b216240.5%
 
h82680.2%
 
k38160.1%
 
v1908< 0.1%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_284928100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
19730829.5%
 
24579213.9%
 
33943212.0%
 
4324369.8%
 
5267128.1%
 
6241687.3%
 
7216246.6%
 
8165365.0%
 
9133564.1%
 
0120843.7%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin396228086.6%
 
Common61437613.4%
 

Most frequent Latin characters

ValueCountFrequency (%) 
r55014013.9%
 
a45601211.5%
 
l3777849.5%
 
e3485288.8%
 
i3148207.9%
 
t3014647.6%
 
o2957407.5%
 
p2486766.3%
 
n2143325.4%
 
s1882564.8%
 
u1666324.2%
 
c1303803.3%
 
m1055762.7%
 
f1055762.7%
 
d826802.1%
 
g400681.0%
 
b216240.5%
 
h82680.2%
 
k38160.1%
 
v1908< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
_28492846.4%
 
19730815.8%
 
2457927.5%
 
3394326.4%
 
4324365.3%
 
5267124.3%
 
6241683.9%
 
7216243.5%
 
8165362.7%
 
9133562.2%
 
0120842.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII4576656100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
r55014012.0%
 
a45601210.0%
 
l3777848.3%
 
e3485287.6%
 
i3148206.9%
 
t3014646.6%
 
o2957406.5%
 
_2849286.2%
 
p2486765.4%
 
n2143324.7%
 
s1882564.1%
 
u1666323.6%
 
c1303802.8%
 
m1055762.3%
 
f1055762.3%
 
1973082.1%
 
d826801.8%
 
2457921.0%
 
g400680.9%
 
3394320.9%
 
4324360.7%
 
5267120.6%
 
6241680.5%
 
b216240.5%
 
7216240.5%
 
Other values (6)559681.2%
 

degree
Real number (ℝ≥0)

Distinct440
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean89.40178571
Minimum0
Maximum440
Zeros402
Zeros (%)0.1%
Memory size4.3 MiB
2020-10-13T14:29:39.616877image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile22
Q153
median79
Q3115
95-th percentile192
Maximum440
Range440
Interquartile range (IQR)62

Descriptive statistics

Standard deviation53.33627971
Coefficient of variation (CV)0.5965907647
Kurtosis2.887237781
Mean89.40178571
Median Absolute Deviation (MAD)30
Skewness1.324156321
Sum25473072
Variance2844.758733
MonotocityNot monotonic
2020-10-13T14:29:39.731874image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
6127631.0%
 
6527421.0%
 
6327391.0%
 
7027341.0%
 
6227311.0%
 
6627311.0%
 
6727201.0%
 
7227071.0%
 
7126910.9%
 
7326860.9%
 
6826690.9%
 
7726620.9%
 
5926520.9%
 
6926520.9%
 
5726340.9%
 
7626330.9%
 
7926260.9%
 
6026260.9%
 
6426190.9%
 
5626090.9%
 
5826020.9%
 
5525990.9%
 
7425950.9%
 
8025550.9%
 
5225520.9%
 
Other values (415)21839976.7%
 
ValueCountFrequency (%) 
04020.1%
 
12960.1%
 
23280.1%
 
33380.1%
 
43680.1%
 
54040.1%
 
64660.2%
 
75070.2%
 
85230.2%
 
94980.2%
 
ValueCountFrequency (%) 
4402< 0.1%
 
4391< 0.1%
 
4381< 0.1%
 
4371< 0.1%
 
4363< 0.1%
 
4341< 0.1%
 
4333< 0.1%
 
4322< 0.1%
 
4313< 0.1%
 
4303< 0.1%
 

hemisphere
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
lh
143100 
rh
141828 
ValueCountFrequency (%) 
lh14310050.2%
 
rh14182849.8%
 
2020-10-13T14:29:39.830550image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-13T14:29:39.884657image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:29:39.950583image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
h28492850.0%
 
l14310025.1%
 
r14182824.9%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter569856100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
h28492850.0%
 
l14310025.1%
 
r14182824.9%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin569856100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
h28492850.0%
 
l14310025.1%
 
r14182824.9%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII569856100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
h28492850.0%
 
l14310025.1%
 
r14182824.9%
 

Interactions

2020-10-13T14:28:44.708053image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:44.859246image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:44.988417image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:45.111070image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:45.228202image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:45.347523image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:45.459170image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:45.575499image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:45.700457image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:45.824596image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:45.940265image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:46.046330image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:46.166871image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:46.405465image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:46.522191image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:46.633428image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:46.744242image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:46.858546image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:46.983326image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:47.135019image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:47.274323image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:47.400825image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:47.523623image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:47.656855image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:47.779066image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:47.899946image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:48.031692image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:48.163056image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:48.292478image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:48.446291image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:48.569958image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:48.692067image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:48.816634image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:48.938055image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:49.063159image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:49.187951image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:49.363451image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:49.499428image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:49.623665image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:49.748539image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:49.894401image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:50.055336image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:50.189834image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:50.323080image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:50.455902image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:50.588955image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:50.711407image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:50.841681image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:50.970261image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:51.116194image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:51.383235image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:51.524873image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:51.651968image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:51.771748image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:51.889145image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:52.012475image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:52.142344image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:52.256652image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:52.377335image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:52.504309image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:52.635472image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:52.772275image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:52.900830image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:53.027848image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:53.168182image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:53.302333image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:53.427440image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:53.557669image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:53.681271image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:53.813887image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:53.939340image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:54.063924image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:54.194545image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:54.325561image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:54.444797image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:54.569684image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:54.690722image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:54.824400image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:54.957051image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:55.084865image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:55.207280image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:55.342747image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:55.476582image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:55.611538image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:55.735096image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:55.860628image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:55.987073image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:56.103565image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:56.225445image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:56.342473image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:56.455658image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:56.575297image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:56.696316image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:56.815302image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:28:56.942852image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-10-13T14:29:24.189718image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:29:24.312710image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:29:24.436100image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:29:24.560870image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:29:24.685705image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:29:24.815882image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:29:24.936293image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:29:25.375025image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:29:25.507148image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:29:25.628886image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:29:25.751620image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:29:25.876288image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:29:26.002939image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:29:26.129878image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:29:26.248263image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:29:26.363822image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:29:26.480065image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:29:26.591518image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:29:26.710802image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:29:26.829194image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:29:26.944044image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:29:27.065027image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:29:27.181651image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:29:27.301609image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:29:27.424020image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:29:27.543684image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:29:27.664139image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:29:27.784715image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:29:27.904052image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:29:28.033906image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:29:28.155632image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:29:28.291221image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:29:28.420370image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:29:28.544051image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:29:28.661991image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:29:28.781621image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:29:28.895840image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:29:29.012302image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:29:29.132973image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:29:29.254229image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:29:29.376626image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:29:29.489078image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:29:29.623750image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:29:29.820294image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:29:29.999255image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:29:30.144144image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:29:30.267747image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:29:30.385631image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:29:30.510363image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:29:30.656650image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:29:30.821475image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:29:30.996540image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:29:31.215703image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:29:31.404155image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:29:31.616300image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:29:31.807665image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:29:31.980439image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:29:32.172972image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:29:32.337754image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:29:32.462490image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-10-13T14:29:40.055669image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-10-13T14:29:40.238064image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-10-13T14:29:40.405657image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-10-13T14:29:40.576479image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-10-13T14:29:40.713182image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-10-13T14:29:33.050645image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:29:33.841758image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-13T14:29:34.698841image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

idage_mosage_yrsgenderpeermindsetpersmindsetneedforapprovalneedforbelongingrejectioncoping_madcoping_sadcoping_worriedrsqanxietyrsqangercdimeanmoodgoodmoodhappymoodrelaxedstateanxietytraitanxietybandroidegreehemisphere
0105_9a110.49.14male1.831.52.623.73.02.673.01.676.925.251.04.06.06.031.022.0DCbankssts_137.0lh
1105_9a110.49.14male1.831.52.623.73.02.673.01.676.925.251.04.06.06.031.022.0DCbankssts_1103.0rh
2105_9a110.49.14male1.831.52.623.73.02.673.01.676.925.251.04.06.06.031.022.0DCbankssts_270.0lh
3105_9a110.49.14male1.831.52.623.73.02.673.01.676.925.251.04.06.06.031.022.0DCbankssts_232.0rh
4105_9a110.49.14male1.831.52.623.73.02.673.01.676.925.251.04.06.06.031.022.0DCbankssts_377.0lh
5105_9a110.49.14male1.831.52.623.73.02.673.01.676.925.251.04.06.06.031.022.0DCbankssts_398.0rh
6105_9a110.49.14male1.831.52.623.73.02.673.01.676.925.251.04.06.06.031.022.0DCcaudalanteriorcingulate_195.0lh
7105_9a110.49.14male1.831.52.623.73.02.673.01.676.925.251.04.06.06.031.022.0DCcaudalanteriorcingulate_136.0rh
8105_9a110.49.14male1.831.52.623.73.02.673.01.676.925.251.04.06.06.031.022.0DCcaudalanteriorcingulate_241.0lh
9105_9a110.49.14male1.831.52.623.73.02.673.01.676.925.251.04.06.06.031.022.0DCcaudalanteriorcingulate_225.0rh

Last rows

idage_mosage_yrsgenderpeermindsetpersmindsetneedforapprovalneedforbelongingrejectioncoping_madcoping_sadcoping_worriedrsqanxietyrsqangercdimeanmoodgoodmoodhappymoodrelaxedstateanxietytraitanxietybandroidegreehemisphere
284918531_17a206.7117.12male3.422.03.52.82.52.02.332.337.53.081.16.06.03.030.028.0gammasupramarginal_746.0rh
284919531_17a206.7117.12male3.422.03.52.82.52.02.332.337.53.081.16.06.03.030.028.0gammasupramarginal_8153.0lh
284920531_17a206.7117.12male3.422.03.52.82.52.02.332.337.53.081.16.06.03.030.028.0gammasupramarginal_820.0rh
284921531_17a206.7117.12male3.422.03.52.82.52.02.332.337.53.081.16.06.03.030.028.0gammasupramarginal_9150.0lh
284922531_17a206.7117.12male3.422.03.52.82.52.02.332.337.53.081.16.06.03.030.028.0gammasupramarginal_938.0rh
284923531_17a206.7117.12male3.422.03.52.82.52.02.332.337.53.081.16.06.03.030.028.0gammatemporalpole_1115.0lh
284924531_17a206.7117.12male3.422.03.52.82.52.02.332.337.53.081.16.06.03.030.028.0gammatemporalpole_169.0rh
284925531_17a206.7117.12male3.422.03.52.82.52.02.332.337.53.081.16.06.03.030.028.0gammatransversetemporal_1280.0lh
284926531_17a206.7117.12male3.422.03.52.82.52.02.332.337.53.081.16.06.03.030.028.0gammatransversetemporal_174.0rh
284927531_17a206.7117.12male3.422.03.52.82.52.02.332.337.53.081.16.06.03.030.028.0gammatransversetemporal_2275.0lh